what I would like to see is a parameterized class of prompts which can never be solved by the LLMs even when a finite number of them are manually added to the dataset.
53 karma · joined December 6, 2023
on another note: an entire paper written on one prompt - is this the state of research these days ?
finally: a giant group of data-entry technicians are likely entering these exceptions into the training dataset at openai.
until then, they have a free pass to get away with such scare-mongering bs.
are any startups applying LLMs profitable at all ? or is it just a mirage - ie, in the real world, startups are not able to solve users problems well using LLMs.
Dont we all live in the joyful bubble of beliefs many of which have no basis ?
Claims about out-performance on tasks are just that, claims. the next iteration of llama or mixtral will converge.
LLMs seem to evolve like linux/windows or ios/android with not much differentiation in the foundation models.
that is, the market is perception of what is out there, reinforced by the herd mentality. the reality is what actually exists.
eventually perception and reality tend to converge.
think euclid, galileo, newton, maxwell, etc...
and all human knowledge is mathematical in nature (galileo said this).
what is meant here is that, facts and events in the world we perceive can be compressed into small models which are mathematical in nature and allow a deductive method.
human genius comprises of coming up with these models. This process is described by Peirce (and Kant before him) ie, inventing concepts and relations between them to comprise models of the world we live in.
imagine compressing all observed motion into a few equations of physics. or compress all electromagnetic phenomena into a few equations. and then use this machinery to make things happen.
imagine if we feed a lot of perceived motion data into a giant black-box (which could be a neural net) - and out comes a small model of that data comprising newton's equations (and similarly maxwellian equations).
But, this giant knowledge edifice is built on solid foundations of mathematical reasoning (newton said this).
human genius is to invent a mathematical language to describe imaginary worlds precisely, and then a scientific method to apply that language to model the real world.
the average theorem in euclids' elements (written 2000 years back) would have a reasoning chain of at least 10 steps.
all of the mathematical machinery humans build need 100% accuracy in each step
reasoning requires deterministic symbolic manipulation for accuracy. only then it can be composed into long chains.
knowledge consists of models of the world we have constructed and learnt, which abstract patterns of facts.
facts,narratives make for banter with friends (bonding) but knowledge helps with action (decision).
when reading, demarcate narratives from models, and/or layout the facts against known mental models. this may point to deficits in mental models, or missing models altogether.
most of my reading unfortunately is mindless soaking up of pointless narratives.
customer-service, code-assist, call-center are a few areas which show early promise wherein customers are willing to pay for the added value. outside of these areas, i am yet to see breakthrough applications for which people are willing to pay. let me know if this is mistaken.
those panning for gold (app devs, startups etc) may or may not find it. remains to be seen and i remain skeptical.
ie, things we construct by the computer are deterministic. the turing machine (and other equivalent models like the lambda calculus etc) being the canonical machine that models our computations. Arguably, all human knowledge is symbolic and determistic - even though it may model probabilistic phenomena.
the key is to be able to traverse the abstraction hierarchy all the way from the physics of the hardware to the end-user, and that arguably is what any engineer must learn.
LLMs for code are leaky abstractions. They work many-a-time. But when they break, good luck fixing it.
yann-lecunn also put it well. If something works 95% of the time, and you compose it 10 times, it only works 59% of the time.
In the real world of software engineering, we cannot build on something that works 95% of the time reliably. And LLM apologists will immediately say that code written by humans has bugs too. Of course it does.
dealing with dealers and repair shops is not fun. but gas-cars are still the known-devil - masses understand their issues and are habituated to them. evs come with unknowns which hinder fast mass adoption.
the car maker has an inherent incentive to reduce the lifespan of the vehicle which conflicts with the customer's incentive to extend the lifespan.
All human knowledge is "symbolic". that is, knowledge is a set of abstractions (concepts) along with relations between concepts. As an example, by "knowing" addition is to understand the "algorithm" or operations involved in adding two numbers. reasoning is the act of traversing concept chains.
LLMs dont yet operate at the symbolic level, and hence, it could be argued that they dont know anything. LLM is a modern sophist excelling at language but not at reasoning.
Perhaps becoz, it involves LLM and LLMs are hot, and everyone wants a piece of it.
why lay them off when there is hiring in other teams ?
why not move workers from unproductive projects to more promising ones ?
users interact with pytorch - not with hardware libraries. so, if pytorch can abstract the hardware, users wont care.
all users will care about is dollar cost of doing their work. so expect increasing commoditization of the hardware.
further, almost everyone in the ecosystem has an incentive to commoditize the hardware (users, cloud vendors, etc). over time i see the moat eroding - as the moat does not attach directly to the user.
i can buy locks for the doors in my house, but if thieves can break locks, i need the police too to serve as a deterrent. ie, there needs to be a negative incentive for the thieves too.
have dedicated govt agencies going after the crypto money trail, and disrupt the theives. this happened in the pipeline hack.
otherwise, the thieves will continue thieving with nothing to stop them.
thats why we have the police and other such agencies in the real world.
when thieves go after the commons (libraries/hospitals) - we the public taxpayers have every incentive to demand action of our govt as there is nobody else who can help here.
why are the british taxpayers not demanding action ?
1. there is a perceiver.
2. the perceiver apprehends a concept by its properties which are perceived effects
3. new concepts are created by the perceiver (or existing concepts are updated) by perceived effects
4. the geometry of concepts is the knowledge structure with signs to communicate it
5. the above is basically the scientific method and everything else follows from it